Methods, systems, and computer readable media for utilizing visuomotor error augmentation for balance rehabilitation

ABSTRACT

Methods, systems, and computer readable media for utilizing visuomotor error augmentation for balance rehabilitation are provided. An exemplary method includes displaying a dynamic virtual environment defined by an optical flow, obtaining position data of an anatomical portion of a subject that is virtually traversing the dynamic virtual environment, and using the position data to determine a mediolateral displacement measurement of the subject. The method further includes utilizing the mediolateral displacement measurement to define feedback control loop data, establishing an augmented visual error that dynamically adjusts the dynamic virtual environment, wherein the augmented visual error is based on the feedback control loop data and a predefined visual gain factor, and adjusting the optical flow of the dynamic virtual environment by using the augmented visual error.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the priority benefit of U.S. Provisional PatentApplication Ser. No. 62/990,417 filed Mar. 16, 2020, the disclosure ofwhich is incorporated by reference herein in the entirety.

GOVERNMENT INTEREST

This invention was made with government support under Grant No. AG054797awarded by the National Institutes of Health. The government has certainrights in the invention.

TECHNICAL FIELD

The subject matter described herein relates to the rehabilitative usesof virtual environments and associated measured sensory responses. Moreparticularly, the subject matter described herein relates to methods,systems, and computer readable media for utilizing visuomotor erroraugmentation for balance rehabilitation.

SUMMARY

According to one aspect, the subject matter described herein relates tomethods, systems, and computer readable media for utilizing visuomotorerror augmentation for balance rehabilitation. An exemplary methodincludes displaying a dynamic virtual environment defined by an opticalflow, obtaining position data of an anatomical portion of a subject thatis virtually traversing the dynamic virtual environment, and using theposition data to determine a mediolateral displacement measurement ofthe subject. The method further includes utilizing the mediolateraldisplacement measurement to define feedback control loop data,establishing an augmented visual error that dynamically adjusts thedynamic virtual environment, wherein the augmented visual error is basedon the feedback control loop data and a predefined visual gain factor,and adjusting the optical flow of the dynamic virtual environment byusing the augmented visual error.

In one example of the method, the dynamic virtual environment includes avirtual hallway.

In one example of the method, a foreground mediolateral position of thedynamic virtual environment is adjusted by the augmented visual error.

In one example of the method, the mediolateral displacement measurementis based on a difference of a mediolateral position of the dynamicvirtual environment and a mediolateral position marker of the subject.

In one example of the method, the anatomical portion of the subjectcorresponds to at least one of a seventh cervical vertebrae of thesubject, a global head position of the subject, and a global trunkposition of the subject.

In one example of the method, augmented visual error defines adifference between a virtual perception of trunk motion of the subjectand an actual trunk motion of the subject.

In one example of the method, the method further includes moving aforeground of the dynamic virtual environment based on the feedbackcontrol loop data and the augmented visual error by a value of G.

One exemplary system includes a display device configured to display adynamic virtual environment defined by an optical flow and at least oneposition sensor device configured to obtain position data of ananatomical portion of a subject that is virtually traversing the dynamicvirtual environment. The system further includes a system controllerdevice configured to use the position data to determine a mediolateraldisplacement measurement of the subject, utilize the mediolateraldisplacement measurement to define feedback control loop data, establishan augmented visual error that dynamically adjusts the dynamic virtualenvironment, wherein the augmented visual error is based on the feedbackcontrol loop data and a predefined visual gain factor, and adjust theoptical flow of the dynamic virtual environment by using the augmentedvisual error.

In one example of the system, the dynamic virtual environment includes avirtual hallway.

In one example of the system, a foreground mediolateral position of thedynamic virtual environment is adjusted by the augmented visual error.

In one example of the system, the mediolateral displacement measurementis based on a difference of a mediolateral position of the dynamicvirtual environment and a mediolateral position marker of the subject.

In one example of the system, the anatomical portion of the subjectcorresponds to at least one of a seventh cervical vertebrae of thesubject, a global head position of the subject, and a global trunkposition of the subject.

In one example of the system, augmented visual error defines adifference between a virtual perception of trunk motion of the subjectand an actual trunk motion of the subject.

In one example of the system, the system controller device is furtherconfigured to move a foreground of the dynamic virtual environment basedon the feedback control loop data and the augmented visual error by avalue of G.

The subject matter described herein may be implemented in hardware,software, firmware, or any combination thereof. As such, the terms“function” “node” or “engine” as used herein refer to hardware, whichmay also include software and/or firmware components, for implementingthe feature being described. In one exemplary implementation, thesubject matter described herein may be implemented using a computerreadable medium having stored thereon computer executable instructionsthat when executed by the processor of a computer control the computerto perform steps. Exemplary computer readable media suitable forimplementing the subject matter described herein include non-transitorycomputer-readable media, such as disk memory devices, chip memorydevices, programmable logic devices, and application specific integratedcircuits. In addition, a computer readable medium that implements thesubject matter described herein may be located on a single device orcomputing platform or may be distributed across multiple devices orcomputing platforms.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter described herein will now be explained with referenceto the accompanying drawings of which:

FIG. 1 illustrates a diagram of an exemplary system for utilizingvisuomotor error augmentation for balance rehabilitation according to anembodiment of the subject matter described herein;

FIG. 2 illustrates a plurality of graphs exhibiting the mediolateraltrunk motion of individual subjects in response to variouserror-augmented optical flows according to an embodiment of the subjectmatter described herein;

FIG. 3 illustrates a plurality of graphs exhibiting stride-averagedmediolateral seventh cervical vertebrae positions associated withvarious error augmented optical flows according to an embodiment of thesubject matter described herein;

FIG. 4 illustrates graphs exhibiting intra-step outcome measurementsthat quantify head and trunk motion during walking according to anembodiment of the subject matter described herein;

FIG. 5 illustrates graphs exhibiting inter-step outcome measurementsthat quantify head and trunk motion during walking according to anembodiment of the subject matter described herein;

FIG. 6 illustrates a exemplary system diagram configured for utilizingvisuomotor error augmentation for balance rehabilitation according to anembodiment of the subject matter described herein;

FIG. 7 illustrates a block diagram of the primary level of an exemplaryreal-time controller configured to utilize visuomotor error augmentationfor balance rehabilitation according to an embodiment of the subjectmatter described herein;

FIG. 8 illustrates a block diagram of the secondary level of anexemplary real-time controller configured to utilize visuomotor erroraugmentation for balance rehabilitation according to an embodiment ofthe subject matter described herein; and

FIG. 9 illustrates a flow chart of a method for utilizing visuomotorerror augmentation for balance rehabilitation according to an embodimentof the subject matter described herein.

DETAILED DESCRIPTION

Prior work demonstrates that humans spontaneously synchronize their headand trunk kinematics to a broad range of driving frequencies ofperceived mediolateral motion prescribed using optical flow. Using aclosed-loop visuomotor error augmentation task in an immersive virtualenvironment, it was sought to understand whether unifying visualfeedback with vestibular and somatosensory feedback is a control goalduring human walking, at least in the context of head and trunkstabilization. It was hypothesized that humans would minimize visualerrors during walking—i.e., those between the visual perception ofmovement and actual movement of the trunk. Notably, subjects did notminimize errors between the visual perception of movement and actualmovement of the head and trunk. Rather, subjects increased mediolateraltrunk range of motion in response to error-augmented optical flow withpositive feedback gains. The results are more consistent with thealternative hypothesis

-   -   that visual feedback can override other sensory modalities and        independently compel adjustments in head and trunk position.        Also, aftereffects following exposure to error-augmented optical        flow included longer, narrower steps and reduced mediolateral        postural sway, particularly in response to larger amplitude        positive feedback gains. The results allude to a recalibration        of head and trunk stabilization toward more tightly regulated        postural control following exposure to error-augmented visual        feedback. Lasting reductions in mediolateral postural sway may        have implications for using error-augmented optical flow to        enhance the integrity of walking balance control through        training, for example in older adults and any person with        balance deficits, including neurodegenerative disease (e.g.,        Multiple sclerosis). Indeed, any population with peripheral        sensation loss and/or unreliable distal sensation could benefit        from the disclosed subject matter.

1. Introduction

Humans regulate lateral balance in walking through coordinatedadjustments between the continuous control of posture (i.e., head andtrunk stabilization) and the discrete (step-to-step) control of footplacement (i.e., step width). Here, successful coordination depends onappropriate motor planning and execution, which in turn depend on havingaccurate and reliable sensory feedback. Optical flow perturbations, aclass of experimental paradigms used in the study of walking balancecontrol, are unique in that they exclusively target that sensoryfeedback through the visual perception of lateral imbalance. Somewhatsurprisingly, walking balance is acutely susceptible to thoseperturbations, and the resulting motor responses may have the capacityto inform how sensory feedback is used in the planning and execution ofstable locomotion. For example, head and trunk kinematics during walkingspontaneously synchronize (i.e., entrain) to a broad range of drivingfrequencies of perceived mediolateral (ML) motion prescribed usingoptical flow. The intuitive interpretation of those findings is thatsuch entrainment may act to minimize errors between the visualperception of motion and the actual motion of the head and trunk,thereby unifying visual with vestibular and somatosensory feedback.However, direct evidence that minimizing these “visual errors” is acontrol goal for head and trunk stabilization during human walking iscurrently lacking.

Head and trunk stabilization is critical for regulating walking balance.Usually thought of as arising from corrective motor responses governedby vestibular feedback, this process is more likely governed by theintegration of sensory cues from both visual and vestibular feedback.For example, while vestibular feedback provides a spatial reference foreffective head and trunk stabilization during walking, such stabilizingalso provides a reliable visual reference for regulating foot placement,navigating complex environments, and avoiding obstacles. In addition,postural deviations that occur normally in walking influence not onlythe spatial reference for head and trunk stabilization but also opticalflow—the visual perception of self-motion, the relative motion ofobjects in the environment, or both. Indeed, some studies have shownevidence that visual feedback, and optical flow, in particular, play animportant role in both postural stability and navigation during walking.However, it remains unclear how this visual perception of self-motion,in concert with cues from other sensory modalities, is integrated tostabilize the head and trunk during walking—knowledge with particularrelevance to navigating unstable environments that could challengewalking balance.

In the neural control of movement, sensory errors arise when actualsensory feedback cues differ from those anticipated to follow from agiven motor command. Also, on-line monitoring of sensory errors canindependently drive motor corrections, for example, decreasing thedifference between the visual perception of movement and actual movementof the limb during an arm reaching task. Different from the optical flowperturbations used to study entrainment, error-augmentation is tied tothe subjects' own performance and is thus more analogous to abiofeedback paradigm. In walking, a similar process of errorminimization could provide a logical explanation for why peoplesynchronize (i.e., entrain) their head and trunk movements to even verycomplex mediolateral optical flow oscillations. Specifically, the onsetof such oscillations, for example in the context of optical flowperturbation studies, introduces errors between the visual perception ofself-motion and the actual motion of the head and trunk. Indeed, it waspreviously proposed that the synchronization of motor responses tovisual stimuli during walking is goal-directed, alluding to a process oferror minimization wherein proprioceptive and vestibular cues becomemore consistent with perceived mediolateral motion. However, whilepseudorandom optical flow perturbations can elicit visuomotorentrainment, they are poorly equipped to provide mechanistic insightinto its origin.

Error-augmentation is a paradigm in which movement errors are measuredand augmented from an intended trajectory with the goal of strengtheningmovement control. Although not yet explicitly applied to the visuomotorcontrol of head and trunk motion in walking, the paradigm has a richhistory in the sensorimotor control literature, particularly in armreaching tasks.

Largely pioneered by Patton and colleagues, augmenting (i.e.,increasing) sensory errors has been shown to effectively elicit motoradaptation while providing mechanistic insight into the origins of thatadaptation. Regarding the role of vision in governing lateral balance inwalking, there is a presumption that an overriding task goal in whichspatial differences between actual motion of the head and trunk andsensory cues via optical flow are minimized. With error-augmentation,those visual errors can be systematically manipulated in real-time tounderstand their role in governing head and trunk position and thusstabilization during walking. Moreover, given its effect on motorlearning and adaptation in arm reaching tasks, error-augmentation—herein the context of optical flow—may also recalibrate head and trunkcontrol in walking toward the presence of after-effects followingprolonged exposure.

Therefore, the purpose of this study was to investigate the role ofvisual errors in governing the sensorimotor control of head and trunkposition during human walking as a means to explain the acute posturalresponse to optical flow perturbations. A closed-loop visuomotor erroraugmentation task in an immersive virtual environment is used tointroduce errors between the visual perception of self-motion and actualinstantaneous motion of the head and trunk. The primary hypothesis thatminimization of visual errors was tested, achievable during this taskonly by way of reduced lateral head and trunk movement, is an importantand spontaneous feature governing the visuomotor control of humanlocomotion. The alternative hypothesis would be that visual feedbackoverrides other sensory modalities and is itself an independent controlparameter in governing head and trunk position. To test this alternativehypothesis, the experimental paradigm was designed to include bothpositive (i.e., visual perception that amplified instantaneous head andtrunk motion) and negative (i.e., visual perception that counteractedinstantaneous head and trunk motion) visual feedback gains.

2. Materials and Methods

2.1. Subjects

Twelve (12) subjects were recruited in this study (8 males, 4 females,age: 24.1±4.7 yrs., body mass: 73.3±13.0 kg; height: 176±9 cm,mean±standard deviation, S.D.). All subjects were healthy without anycurrent neuromusculoskeletal disorders or injuries. Each subjectprovided written informed consent according to the approved protocolwith the Biomedical Sciences Institutional Review Board of theUniversity of North Carolina at Chapel Hill.

2.1.1. Experimental protocol and data collection

A photocell timing system (Brower Timing Systems, Draper, UT) was firstused to measure subjects' preferred overground walking speed (PWS) fromthe average of three durations taken to traverse the middle 2 m of a 10m walkway at their normal, comfortable walking speed (1.36±0.14 m·s⁻¹).The subjects' PWS was established using an overground walking paradigm,which may yield a different speed than that using a treadmill walkingparadigm. All subjects then walked at their PWS on an instrumentedsplit-belt treadmill (1.45 m long×0.60 m wide belts, Bertec Corp.,Columbus, Ohio). For all treadmill walking trials, subjects watched aspeed-matched, immersive virtual hallway rear-projected onto asemi-circular screen (1.45 m radius×2.54 m height, see projection screen102 in FIG. 1) surrounding the treadmill 104. Thirty (30)retroreflective markers were placed, including those on anatomicallandmarks and marker clusters, on the 7th cervical vertebra (C7),pelvis, and right and left foot, shank, and thigh of the subject 106.Alternatively, the anatomical landmark may include to general areas ofanatomical portions of the subject, such as a global head position ofthe subject and/or a global trunk position of the subject.

A 3D motion capture system (Motion Analysis Corp., Santa Rosa, Calif.,10 cameras) recorded the trajectories of each marker at 100 Hz. Themediolateral position of the C7 marker was chosen for the feedbackcontrol loop of the virtual hallway because C7 is the highest point onthe body with large translation while not affected by head orientation.In some embodiments, the mediolateral displacement measurement is basedon a difference of a mediolateral position of the dynamic virtualenvironment and an mediolateral position marker corresponding tospecific anatomical points, such as the C7 vertebrae of the subject, orto general areas such as a global head position of the subject and/or aglobal trunk position of the subject. For example, the 3D motion capturesystem can be configured to capture image data of the global headposition and/or global trunk position of the subject that can be used asthe position data of the anatomical portion of the subject.

Mediolateral optical flow in the virtual environment was augmented inreal-time based on instantaneous measurements of subjects' trunkposition as follows. The mediolateral position of the C7 marker wasstreamed from the motion capture system through local Ethernet toanother computer and received using a Simulink® real-time controller.The midline of the virtual hallway was set for all subjects as themiddle of the treadmill, with mediolateral variations prescribed tomatch the C7 marker trajectory. Specifically, the end of the hallwayalways remained relatively stationary, while the foreground (e.g.,foreground mediolateral position) moved according to this feedbackparadigm, thereby emulating the subject's head and trunk positionchanges from one step to the next rather than heading corrections. Insome trials, the mediolateral position of the virtual hallway wasaugmented by a factor (G) times the instantaneous mediolateral C7position, thereby introducing an error between the visual perception ofself-motion and the actual motion of the head and trunk of the subject106. The factor G is thereby considered the gain defining the visualerror magnitude which, in different trials, took four values (i.e.,±2.5, ±5.0). The 5.0 magnitude gain was determined in pilot testing tobe the largest possible while ensuring that the virtual hallway remainedon the projection screen. Positive/negative gains indicate virtualhallway mediolateral motion was in the same/opposite direction of theinstantaneous mediolateral C7 motion, respectively. The feedback delaybetween the C7 marker position and resulting changes to virtual hallwaymeasured ˜14 milliseconds using the available Software DevelopmentToolkit (Motion Analysis Corp.). Subjects were verbally instructed toonly “walk on the treadmill while watching the hallway” to recordnaturally emergent patterns in response to error-augmented optical flow.

Subjects first completed one 3-minute trial at their PWS with zero gain(“Baseline”). Subjects then completed four 11-minute walking trials infully randomized order that incorporated error-augmented optical flow(i.e., “adaptation”). As shown in diagram 110 in FIG. 1, each of thosewalking trials consisted of 10 minutes in the presence of an erroraugmentation gain on optical flow followed by 1 min of walking with zerogain (i.e., “Post-adaptation”). At the beginning of each 11-min trial,subjects walked for 15 seconds with a fixed optical flow to reach asteady state walking pattern on the treadmill 104.

2.1.2. Data analysis

The C7 marker was used as a surrogate for the trunk motion that isinsensitive to the head turns. The C7 marker's trajectories werefiltered using a 4th-order zero-lag low-pass digital Butterworth filterwith a cutoff frequency of 8 Hz. Dependent variables for head and trunkposition included the step-to-step range of mediolateral trunk motion(intra-step measure, see FIG. 4), and the root means square (RMS) ofmediolateral trunk position (inter-step measure, see FIG. 5). The timeseries of step length and step width were also calculated to resolvestep-to-step adjustments in foot placement. Specifically, step lengthswere calculated as the relative anterior-posterior position ofconsecutive heel markers at heel strike plus the treadmill belttranslation during that step. Step widths were calculated as themediolateral distance between consecutive heel positions at heel strike.From their time series, mean step length and step width and theirrespective variabilities were calculated—the latter reported as thestandard deviation as shown in Tables 1 and 2 below. Notably, Table 1(positive feedback gains) and Table 2 (negative feedback gains) show theeffects of error-augmented optical flow on foot placement kinematics(cm).

TABLE 1 Positive feedback gains (G⁺) 5.0 2.5 SW (14.8 ± 3.8) Early 15.0± 5.2  15.1 ± 5.6  Middle 14.6 ± 4.6  14.0 ± 4.5  Late 14.6 ± 5.3  14.5± 5.8  post 12.1 ± 3.9* 13.1 ± 4.7* SW (70.5 ± 6.3) Early 70.3 ± 7.1 70.3 ± 6.8  Middle 70.6 ± 7.2  71.0 ± 6.7  Late 70.7 ± 7.1  71.4 ± 6.5 post 71.6 ± 6.9* 71.8 ± 6.9* SW (2.3 ± 0.6) Early  2.7 ± 0.7   2.5 ±0.5  Middle  2.9 ± 0.9*  2.7 ± 0.6* Late  3.0 ± 0.9*  2.8 ± 1.1* post 2.6 ± 0.7   2.5 ± 0.7  SW (2.1 ± 0.6) Early  2.2 ± 0.7   2.1 ± 0.7 Middle  2.1 ± 0.4   1.8 ± 0.5  Late  2.1 ± 0.6   2.1 ± 1.0  post  1.9 ±0.5   1.9 ± 0.7 

TABLE 2 Negative feedback gains (G⁻) 5.0 2.5 SW (14.8 ± 3.8) Early 15.3± 5.0  15.3 ± 5.2 Middle 13.7 ± 4.5  13.7 ± 4.2 Late 14.1 ± 4.9  13.9 ±4.5 post 15.4 ± 4.9  14.5 ± 5.1 SW (70.5 ± 6.3) Early 70.1 ± 6.8  69.5 ±5.7 Middle 71.0 ± 6.7  70.9 ± 6.6 Late 70.9 ± 6.7  71.1 ± 6.8 post 70.3± 6.7  70.8 ± 6.8 SW (2.3 ± 0.6) Early  2.9 ± 0.9   2.5 ± 0.6 Middle 2.9 ± 0.8*  2.5 ± 0.9 Late  3.0 ± 0.8*  2.7 ± 0.9 post  2.4 ± 0.6   2.5± 0.9 SW (2.1 ± 0.6) Early  2.2 ± 0.5   2.1 ± 0.7 Middle  2.0 ± 0.5  1.9 ± 0.6 Late  1.9 ± 0.5   2.0 ± 0.6 post  2.0 ± 0.6   1.9 ± 0.7

As indicated above, Tables 1 and 2 illustrate the effects of erroraugmented optical flow on foot placement kinematics (cm). The datarepresented in Tables 1 and 2 are mean±standard deviation. Notationsrepresented in Tables 1 and 2 are as follows: SW: step width, SL: steplength, SWV: step width variability, SLV: step length variability.Baseline values from normal walking are shown in parentheses. Asterisks(*) indicate significantly different (p<0.05) from baseline walking.

2.2. Statistical analysis

First, pairwise t-tests were used to compare dependent variables fromthe last minute of the 3-min baseline walking trial to those extractedfrom walking with error-augmented optical flow (min 1 [“Early”], min 5[“Middle”], min 10 [“Late”] in adaptation), including after-effects fromso. For any pairwise comparison in the texts, effect size is reported asCohen's d. Second, two, 2-way repeated measure ANOVAs were utilized todetermine the effects of and interactions between Magnitude (i.e., 2.5,5.0) and Phase (i.e., min 1-“Early”, min 5-“Middle”, and min 10-“Late”)for error-augmented optical flow with: (i) positive and (ii) negativefeedback gains including the effect size (η²). When a significant maineffect or interaction was found, post-hoc pairwise comparisons wereperformed to identify which conditions produced those effects. ABonferroni correction adjusted the level of significant main effect forpost-hoc pairwise comparisons with a critical alpha level of 0.0085. Inaddition, the three tests conducted within each 2-way repeated measureANOVA may cause alpha inflation. This problem was mitigated using twoprocedures: (i) the sequential Bonferroni (seqB) correction procedurewas used to control the familywise error rate (FWER) by evaluating eachnull hypothesis against an a level adjusted to control for the inflatedprobability of a Type I error; (ii) the Benjamini-Hochberg (BH)procedure was used to control the false discovery rate (FDR, Type IIerror). All statistics were coded in MATLAB (MathWorks Inc., Natick,Mass.). The results of these procedures are summarized in AppendixTables A and B (see below).

3. Results

3.1. The effects of error-augmented optical flow on trunk motion

Mediolateral trunk motion from individual subjects during their initialresponse to error-augmented optical flow compared to baseline walking issummarized in FIG. 2, with stride-average profiles shown in FIG. 3. FIG.3 illustrates a plurality of graphs 301-304 exhibiting stride-averagedmediolateral C7 vertebrae positions associated with various erroraugmented optical flows according to an embodiment of the subject matterdescribed herein. Notably, stride-averaged mediolateral (ML) C7 positionduring baseline walking (solid gray line) compared to early exposure(black solid line) and after-effects following cessation oferror-augmented optical flow (gray dotted line, i.e., post) for visualgains of G=+5.0 (as shown in graph 301), G=+2.5 (as shown in graph 302),G=−2.5 (as shown in graph 303), and G=−5.0 (as shown in graph 304). Eachcurve was first averaged across all steps taken in that respective phaseand then averaged across all subjects. Zero on the ordinate refers tothe midline of the treadmill.

FIG. 4 illustrates graphs exhibiting intra-step outcome measurementsthat quantify head and trunk motion during walking according to anembodiment of the subject matter described herein. Notably, this figureillustrates the intra-step outcome measurement that quantifies asubject's head and trunk motion during walking. FIG. 4 shows the groupaverage (standard deviation) range of step-to-step mediolateral trunkposition at different phases (early, middle, and late) of exposure toerror-augmented optical flow with (A) positive (see graph 402) and (B)negative (see graph 404) visual gains compared to baseline walking andafter-effects following cessation (post). Asterisks (*) indicatesignificant (p<0.05) difference compared to baseline walking. The maineffects and interaction terms from the repeated measures ANOVA duringexposure are included. The corresponding F values and effect sizes (η²)for these statistical comparisons follow: F_(mag)(1,11)=0.22, η_(mag)²=0.02; F_(phase)(2,22)=0.16, η_(phase) ²=0.01; F_(mag*phase)(2,22)=3.34, η_(mag*phase) ²=0.23 in panel A. F_(mag)(1,11)=4.64,η_(mag) ²=0.30; F_(phase)(2,22)=2.29, η_(phase) ²=0.17; F_(mag*phase)(2,22)=0.14, η_(mag*phase) ²=0.01 in panel B.

Similarly, FIG. 5 illustrates graphs exhibiting inter-step outcomemeasurements that quantify head and trunk motion during walkingaccording to an embodiment of the subject matter described herein.Notably, this figure illustrates the inter-step outcome measurement thatquantifies a subject's head and trunk motion during walking. The figureshows the group average (standard deviation) root mean square (RMS) ofmediolateral (ML) trunk position at different phase (early, middle, andlate) of exposure to error-augmented optical flow with (A) positive and(B) negative visual gains compared to baseline walking and after-effectsfollowing cessation (post). Asterisks (*) indicate significant (p<0.05)difference compared to the baseline. The main effects and interactionterms from the repeated measures ANOVA during exposure are included. Thecorresponding F values and effect sizes η2) for these statisticalcomparisons follow: F_(mag)(1,11)=4.56, η_(mag) ²=0.29;F_(phase)(2,22)=1.29, η_(phase) ²=0.11; F_(mag*phase) (2,22)=1.04,η_(mag*phase) ²=0.09 in panel A. F_(mag)(1,11)=2.97, η_(mag) ²=0.21;F_(phase)(2,22)=0.61, η_(phase) ²=0.05; F_(mag*phase) (2,22)=0.77,η_(mag*phase) ²=0.07 in panel B.

Neither intra- (p-values>0.066, see FIG. 4) nor inter-step(p-values>0.162, see FIG. 5) measures of mediolateral trunk motionmagnitude decreased in the presence of error-augmented optical flow.Rather, compared to baseline, positive visual errors increased the RMSof mediolateral trunk position (early vs. baseline; G=+2.5: F(1,11)=10.27, p=0.008, Cohen's d=0.73, G=+5.0: F(1, 11)=7.90, p=0.017,d=1.02, graph 502 in FIG. 5). Moreover, this effect on the inter-stepmeasure of trunk motion scaled in proportion to feedback gain magnitude(main effect, p=0.056). The intra-step measure, step-to-stepmediolateral trunk range of motion magnitude, was unaffected by thepresence of error-augmented optical flow with positive gains (early vs.baseline; G=+2.5: F(1, 11)=0.07, p=0.803, d=−0.03, G=+5.0: F(1,11)=1.05, p=0.327, d=−0.15, see graph 402 in FIG. 4). These earlyresponses to error-augmented optical flow with positive feedback gainswere relatively stable; no significant phase effects were found onstep-to-step mediolateral trunk range of motion (F(2, 22)=0.16, p=0.854,η²=0.01, see graph 402 in FIG. 4) nor the RMS of mediolateral trunkposition (F(2, 22)=1.29, p=0.294, η²=0.11, see graph 502 in FIG. 5).

Compared to baseline, negative visual errors had no effect on intra-step(early vs. baseline; G=−2.5: F(1, 11)=0.32, p=0.586, Cohen's d=−0.07,G=−5.0: F(1, 11)=0.15, p=0.704, d=0.05, see graph 404 in FIG. 4) norinter-step (see graph 504 in FIG. 5, early vs. baseline, F(1, 11)=0.77,p=0.399, d=−0.23, G=−2.5, F(1, 11)=0.49, p=0.500, d=0.13, G=−5.0)measure of mediolateral trunk motion. In addition, no main effect oftime (i.e., phase) were found on either outcome measure (p-values>0.125,see graph 404 in FIG. 4 and graph 504 in FIG. 5).

Following the ‘release’ of error-augmented optical flow with positivefeedback gains, the RMS of mediolateral trunk motion returned tobaseline values within one minute (see graph 502 in FIG. 5). However,despite no apparent phase effects during exposure, step-to-stepmediolateral trunk range of motion, the intra-step measure, decreasedsignificantly in Post compared to baseline walking (−12% for G=+5.0,F(1, 11)=17.72, p=0.002, d=−0.35, see graph 402 in FIG. 4; −10% forG=−2.5, F(1, 11)=5.57, p=0.038, d=−0.28, see graph 404 in FIG. 4).

3.2. The effects of error-augmented optical flow on foot placementkinematics

Compared to baseline, no significant immediate effect of error-augmentedoptical flow were found on step length, step width, nor theirvariabilities (early vs. baseline, p-values>0.063, Table 1). Significanteffect of gain magnitude on foot placement kinematics (p-values 0.056,Table 1) were also not found. In contrast, time-dependent changes werefound during prolonged exposure due to an error-augmented optical flowthat was more pronounced for negative than positive visual errors.Specifically, step width (F(2,22)=15.3, p_(phase)=0.002, η²=0.58) andstep length variability (F(2, 22)=8.42, p_(phase)=0.014, η²=0.43)decreased significantly with exposure to negative but not positive gains(Table 1). In contrast, step length increased for both positive (F(2,22)=5.37, p_(phase)=0.041, η²=0.33) and negative (F(2, 22)=27.11,p_(phase)<0.001, η²=0.71) gains during exposure. Finally, following“release” of error-augmented optical flow, after-effects in trunk motionwere accompanied by significantly longer (F(1, 11)=7.07, p=0.02, d=0.17)and narrower (F(1, 11)=25.08, p<0.001, d=—0.71) steps than baseline, butonly following walking with positive gains (Table 1).

4. Discussion

The primary outcome of this study is that humans appear not tospontaneously minimize visual errors, or those between the visualperception of movement and actual movement of the trunk, during walking.The results are instead more consistent with the alternativehypothesis—that visual feedback can override other sensory modalitiesand independently compel adjustments in head and trunk position. Littleevidence was also found that head and trunk kinematics exhibittime-dependent adaptation during prolonged exposure to error-augmentedoptical flow in young adults. However, aftereffects do allude to arecalibration of head and trunk stabilization toward more tightlyregulated postural control following prolonged exposure toerror-augmented optical flow.

4.1. Effects of error-augmented optical flow: exposure versus normalwalking

The effects of error-augmented optical flow differed fundamentally fromthose due to pseudorandom optical flow perturbations, which are morecommonly used in the study of balance in walking. For example, comparedto normal, unperturbed walking, optical flow perturbations can elicittwo- to four-fold increases in foot placement variability and completelydecorrelate the step-to-step structure of step width. In contrast,error-augmented optical flow elicited only subtle changes in footplacement variability. This suggests that lateral balance control inwalking is uniquely susceptible to perturbations designed to enhance thevisual perception of lateral instability, and not merely to generalizederrors in the visual perception of self-motion. Nevertheless, visuomotorcontrol in walking is inherently closed-loop, and error-augmentedoptical flow affected that control during exposure in ways that informthe broader understanding of walking balance and response toperturbations.

The young adults in the study were unable to, or at least did not,maintain their mediolateral trunk motion near the middle of thetreadmill walking surface following exposure to error-augmented opticalflow. The optical flow paradigm used the treadmill's midline as areference for prescribing visual errors.

Specifically, the visual perception of mediolateral motion is augmentedas subjects deviated from the treadmill midline, first due tomediolateral trunk oscillations and then further due to changes inaverage trunk position. Accordingly, in response to positive feedbackgains that would act to “push” them away from the treadmill midline,most individual subject trunk trajectories showed low-frequency drift,each corrected throughout several steps, which was not apparent duringnormal walking as shown in FIG. 2. FIG. 2 illustrates a plurality ofgraphs 201-205 exhibiting the mediolateral trunk motion of individualsubjects in response to various error-augmented optical flows and thebaseline optical flow according to an embodiment of the subject matterdescribed herein. Specifically, the graphs illustrate the medio-lateralposition of the trunk (C7 marker) during early exposure for visual gainsof G=+5.0 (as shown in graph 201), G=+2.5 gain (as shown in graph 202),G=−2.5 (as shown in graph 204), and G=−5.0 (as shown in graph 205)compared to baseline walking (G=0) (as shown in graph 203) for eachsubject.

The “corrections” toward the treadmill midline may be explained bysubjects realizing and responding to the physical bounds set by thewidth of the walking surface. Ultimately, those dynamics likely underliethe increased RMS of mediolateral trunk motion measured in response topositive feedback gains.

There are other possible explanations why error-augmented optical flowwith positive gains, but not negative gains, increased the excursion(i.e., RMS) of mediolateral trunk position. During walking, humans candistinguish the direction to which they are walking (i.e., theirheading) from the direction at which they are looking (e.g., a fixedobject in their environment). When walking down a hallway, humans mayrely on the fixed position of walls in their periphery as an anchoredreference for head and trunk stabilization. In the presence of positivevisual errors, postural deviations to the right would move that anchoredreference of the wall to the right by an amount proportional to feedbackgain magnitude. In this example, only by continuing to move to the rightwould subjects preserve the same relative distance to the anchoredreference. This behavior could form a positive feedback loop that wouldcontinue to “pull” the body toward the right sidewall of the virtualhallway, an influence that would be altogether absent in the presence ofnegative visual errors. Therefore, it is posited that an anchored visualreference, in one scenario the walls of the virtual hallway, provideadditional spatial information for visuomotor control that is leveragedfor head and trunk stabilization.

4.2. Effects of error-augmented optical flow: prolonged exposure andafter-effects

Based on prior evidence that subjects adapt to pseudorandom optical flowperturbations, it is hypothesized that the outcome measures wouldexhibit tuning via time-dependent changes with prolonged exposure. Thedata did not fully support this hypothesis. Subjects' behavioralresponse to error-augmented optical flow was relatively invariant acrossthe duration of each trial. Some evidence suggests that humans adapttheir step-to-step control of step width to regulate mediolateral motionof the trunk, measured here via the C7 marker. This may explain whytime-dependent changes were observed in SWV along with relativelywell-preserved trunk kinematics. Also, time-dependent effects on stepwidth and step length variability, apparent in response to negativegains, were inconsistent across the two amplitudes. Compared to thosefollowing the onset of optical flow perturbations, initial effects weregenerally smaller in response to the present paradigm. In light of thesesmaller effects, one interpretation is that there was less need for orbenefit to adapting to error augmented optical flow during each 10-mintrial. However, it is suspected that some adaptation did occur; ashypothesized, prolonged exposure to error-augmented optical flowelicited aftereffects that persisted following “release” oferror-augmented optical flow. Most notably, these aftereffects includedlonger, narrower steps, and smaller step-to-step mediolateral trunkrange of motion, particularly following exposure to larger amplitudepositive feedback gains. Here, the “release” of error-augmented opticalflow is seen as analogous to catch trials in arm reaching paradigms thatuse error-augmentation. Specifically, the cessation oferror-augmentation is generally designed to reveal changes in theunderlying strategies used to control movement. Accordingly, themeasured aftereffects in the study are interpreted to suggest that headand trunk stabilization had become more tightly regulated followingexposure compared to baseline. Such an outcome would be anticipated iferror-augmented optical flow with positive gains increased the demandsplaced on the postural control system, presumably to maintain head andtrunk position near the midline of the treadmill with smallermediolateral oscillations.

That subjects exhibited at least temporary reductions in step-to-steppostural sway after exposure to error-augmented optical flow may havetranslational implications. Most falls occur during locomotor activitiessuch as walking, during which preserving the body's center of masswithin the mediolateral base of support is important for balanceintegrity. Accordingly, individuals with excessive mediolateral posturalsway may be at a greater risk of lateral instability and falls. Someevidence suggests that visual feedback can facilitate improved motorlearning with beneficial effects. However, it is unclear what type ofoptical flow paradigm is the best. Balance perturbations, for examplevia mediolateral optical flow oscillations, provide the opportunity topractice reactive adjustments with promising effects on balance controland reducing falls risk. Conversely, error-augmented optical flow withpositive feedback gains may lead to more tightly regulated posturalcontrol following exposure. Because perturbations and error augmentationvia optical flow represent fundamentally different paradigms for balancetraining, thereby differing in their elicited responses andaftereffects, specific recommendations for clinical translation arechallenging. The advent and more widespread adoption of wearable andlow-cost virtual reality technology should inspire continued researchtoward determining which optical flow paradigms, or combinationsthereof, can provide the most beneficial effect on balance integrity,for example, in older adults or in patients with neurodegenerativediseases at high risk of falling.

4.3. Limitations

Foremost, the implications of the outcomes for balance control in peoplewith walking balance deficits or those at risk of falls are discussed.This study focused on otherwise healthy young subjects. Accordingly, theresults may not generalize to those populations in the way that waspredicted. In addition, for practical considerations in the study'sdesign, 10-min trials were opted for. The response to longer exposure toerror-augmented optical flow, like what might be expected from atraining paradigm, are difficult to anticipate. The C7 marker provides asurrogate representation of trunk translation. Future work may considerthe complexities of head rotation together with trunk translation.Treadmill walking speed may also be constrained, and the response toerror-augmented optical flow may have differed if subjects were allowedto regulate their walking speed throughout each trial like whennavigating real-world environments. Finally, after-effects reportedherein allude to changes in neuromuscular control that the experimentaldesign was not equipped to fully capture. Future studies involvingelectromyographic recordings, particularly of postural control muscles,may provide important insight into those changes in control.

FIG. 6 illustrates an exemplary system diagram configured for utilizingvisuomotor error augmentation for balance rehabilitation according to anembodiment of the subject matter described herein. For example, FIG. 6depicts a human subject 601 walking on a treadmill device 604 whilewatching an immersive virtual hallway 603 (that is being projected by aprojection device 606 on a projection screen 602) with the continuousoptical flow while motion capture monitoring the instantaneous positionof a 7th cervical vertebrae (C7) marker associated with subject 601.While the following disclosure describes the optical flow as being a‘continuous optical flow’, the disclosed subject matter may also utilizea non-continuous optical flow in some embodiments (e.g., embodiments inwhich the system elicits a one-off type of change that is notnecessarily continuous as the subject is walking). This may resemble asingle wave oscillation affecting a single step. Moreover, while thesystem is described herein as generalized to utilize a projection deviceand projection screen, embodiments that alternatively utilize a displaydevice, such as a wearable virtual reality (VR) headset device or othersimilar portable devices are within the scope of the disclosed subjectmatter.

Using a motion capture device 608 (e.g., and a real-time systemcontroller device 610, the mediolateral position of the projectedvirtual hallway 603 was captured and visually prescribed to match thatinstantaneous C7 position in the mediolateral direction. In someembodiments, motion capture device 608 includes a 3D motion capturesystem (Motion Analysis Corp., Santa Rosa, Calif., 10 cameras)configured to record the trajectories of the subject's mediolateralpositional markers at 100 Hz and the controller device 610 includes aSimulink® real-time controller configured to receive a stream of themediolateral position of the C7 marker from the motion capture device608 via local Ethernet. In some embodiments, a position sensor devicecan be used instead of motion capture device 608 and can comprise asystem including on-board inertial measurement units or devices. Systemcontroller device 610 may include one or more processors 618, such as acentral processing unit (e.g., a single core or multiple processingcores), a microprocessor, a microcontroller, a network processor, anapplication-specific integrated circuit (ASIC), or the like. Systemcontroller device 610 may also include memory 620. Memory 620 maycomprise random access memory (RAM), flash memory, a magnetic diskstorage drive, and the like. In some embodiments, memory 620 may beconfigured to store an error augmentation engine 622, which can beexecuted by processor(s) 618.

In some trials, visual errors were introduced by augmenting themediolateral virtual hallway position by a factor, G, defined as thevisual gain between the visual perception of the subject's trunk motionand the actual motion of the subject's trunk, which took values of ±2.5and ±5.0. Subjects first completed one 3-minute trial at their PWS withzero gain (“Baseline,” G=0). For each of the four previously mentionedgains, the experimental protocol started with zero gain of 15 seconds toacclimate treadmill walking at zero gain (i.e., normal optical flow,G=0) and followed by 10 min of exposure (i.e., adaptation) toerror-augmented optical flow and finally by 1 minute after cessation oferror-augmented optical flow (G=0). Outcome measures were extracted fromthe first (“early”), fifth (“middle”), and tenth (“late”) minute ofwalking with the error-augmented optical flow for analysis.

FIG. 7 illustrates a block diagram of a primary of an exemplaryreal-time controller (e.g., system controller device 610 shown in FIG.6) configured to utilize visuomotor error augmentation for balancerehabilitation according to an embodiment of the subject matterdescribed herein. For example, FIG. 7 depicts a data packet input 702(i.e., C7 marker's position) from a motion capture device, which is on aseparate computer (608), that provides a subject's C7 marker's position.The data packet input may be modified by a constant 708 for scaling, andsubsequently provided to an amplifier 706. Amplifier 706 can beconfigured to amplify the input by applying a predefined gain, G (e.g.,G=±2.5 or ±5.0). The amplified signal is then provided as one of fourinputs into a second layer (represented as block 710) of the systemcontroller device. Block 710 further receives a direction parameter, asubject's body height parameter, and a treadmill speed parameter asinputs.

FIG. 8 illustrates a block diagram of the secondary level of anexemplary real-time controller configured to utilize visuomotor erroraugmentation for balance rehabilitation according to an embodiment ofthe subject matter described herein. In FIG. 8, the augmented error ofthe position of the C7 marker is amp1. Amp 1 is then fed to control themediolateral position of the virtual hallway (i.e., HeadX). The Dir isan angle measured by degrees. The default value of Dir is zero,indicating Amp 1 (i.e., the augmented error of the position of the C7marker) applies in the mediolateral direction (i.e., HeadX). If thevalue of Dir is changed to 90 degrees, it means that the Amp 1 isapplied in the anterior-posterior direction (i.e., HeadY) of the virtualhallway. Specifically, component 801 converts the Dir from degrees toradians; components 802 and 806 project the Amp 1 into the mediolateral(i.e., HeadX) and anterior-posterior (i.e., HeadY) directions,respectively. Component 808 is the speed of the treadmill, which is aconstant determined from user input. The speed of the treadmill isintegrated with respect to time by component 811 to determine theposition of the avatar along the anterior-posterior direction (i.e.HeadY).

FIG. 9 illustrates a flow chart of a method 900 for utilizing visuomotorerror augmentation for balance rehabilitation. In some embodiments,method 900 includes an algorithm that can be stored in memory andexecuted by a processor. In step 902, a dynamic virtual environmentdefined by an optical flow is displayed (e.g., via projection or adisplay device, such as a wearable VR headset device). In someembodiments, the subject walked on a treadmill while viewing a virtualhallway that is characterized by continuous optical flow (or in someembodiments, a non-continuous optical flow). The dynamic virtualenvironment, such as the virtual hallway, is projected by one or moreprojection devices (or displayed in a wearable VR headset device orother portable display device).

In step 904, position data of an anatomical portion associated with asubject that is virtually traversing the dynamic virtual environment isobtained. In some embodiments, a motion capture device is configured tomonitor the instantaneous position of the C7 vertebrae of the subject.For example, a mediolateral position marker may be placed on the subjecton or near the C7 vertebrae. In other embodiments, the subject may weara position sensor on the C7 vertebrae (or any other selected anatomicallocation). In some embodiments, the position data may include motioncapture data obtained from a motion capture device or position sensordata obtained from a small inertial measurement unit (IMU).

In step 906, the position data is used to determine a mediolateraldisplacement measurement of the subject based on a difference of amediolateral position of the dynamic virtual environment and amediolateral position marker of the subject. In some embodiments, themotion capture device forwards the position data to a system controllerdevice. The system controller device is then configured to utilize theposition data to measure the distance (or differential) between themediolateral position of the C7 marker and the mediolateral position ofthe projected virtual hallway.

In step 908, the mediolateral displacement measurement is utilized todefine feedback control loop data. In some embodiments, the systemcontroller device uses the difference between the mediOlateral positionof the C7 marker and the mediolateral position of the projected virtualhallway as feedback control loop data. The feedback control loop data isprovided as an adjustment signal to the display controller to adjust themediolateral position of the displayed virtual hallway to increase theuser's stabilization error.

In step 910, an augmented visual error that dynamically adjusts thedynamic virtual environment is established, wherein the augmented visualerror is based on the feedback control loop data and a predefined visualgain factor. In some embodiments, the system controller device utilizesthe feedback control loop data and a visual gain factor “G” as inputs toan algorithm that produces an augmented visual error. Notably, theaugmented visual error can be used by the system controller device todynamically adjust the dynamic virtual environment. In some embodiments,the augmented visual error constitutes an error between the visualperception of self-motion and the actual motion of the subject's headand trunk.

In step 912, the optical flow of the dynamic virtual environment isadjusted by using the determined augmented visual error. In someembodiments, the system controller device provides the augmented visualerror as signal data to the projection device (or wearable VR headsetdevice or other portable display device), which in turn adjusts thedynamic virtual environment by modifying the continuous optical flow (ornon-continuous optical flow) that is perceived by the subject.

5. Conclusions

It is respectfully submitted that this is the first study to apply anerror-augmentation paradigm to understand the role of visual errors ingoverning head and trunk stabilization during walking. In contrast tothe earlier explanation for the mechanism governing visuomotorentrainment to optical flow perturbations, young subjects in the studydid not respond consistently to minimize the errors between visualperception of movement and actual movement of the head and trunk. Thus,it cannot be concluded that unifying visual with vestibular andsomatosensory feedback is always a universal control goal in humanwalking, at least in the context of head and trunk stabilization.Rather, visual feedback appears to override other sensory modalities andindependently compel adjustments in head and trunk position. Finally,the results also have important translational implications. Althoughthere was a focus on young adults, aftereffects in the form of reducedmediolateral postural sway evident in the data may have importantimplications for the use of error-augmented optical flow to enhance theintegrity of walking balance control through training, for example inolder adults.

6. Appendix

Results from the sequential Bonferroni (seqB) and Benjamini-Hochberg(BH) procedures for the data included in Tables 1 and 2 above arepresented below in Table A (for positive feedback gains) and Table B(for negative feedback gains).

In Tables A and B, the following notations are used: SW: step width, SL:step length, SWV: step width variability, SLV: step length variability.M: magnitude, P: Phase, M×P: the interaction between Magnitude andPhase. a_(adj)seqB=the adjusted alpha level with the sequentialBonferroni procedure; a_(adj) BH=the adjusted alpha level with theBenjamini-Hochberg procedure; H₀ seqB=evaluation of the null hypotheseswith the sequential Bonferroni procedure; H₀ BH=evaluation of the nullhypotheses with the Benjamini-Hochberg procedure.

TABLE A Positive feedback gains (G⁺) Effect p value α_(adj)seqBα_(adj)BH H₀ seqB H₀ BH SW M 0.513 0.0500 0.0500 Retained Retained P0.229 0.0250 0.0333 Retained Retained M × P 0.227 0.0167 0.0167 RetainedRetained SL M 0.248 0.0500 0.0500 Retained Retained P 0.041 0.01670.0167 Retained Retained M × P 0.092 0.0250 0.0333 Retained Retained SWVM 0.135 0.0167 0.0167 Retained Retained P 0.495 0.0250 0.0333 RetainedRetained M × P 0.927 0.0500 0.0500 Retained Retained SLV M 0.339 0.05000.0500 Retained Retained P 0.128 0.0167 0.0167 Retained Retained M × P0.160 0.0250 0.0333 Retained Retained

TABLE B Negative feedback gains (G⁻) Effect p value α_(adj)seqBα_(adj)BH H₀ seqB H₀ BH SW M 0.875 0.0500 0.0500 Retained Retained P0.002 0.0167 0.0167 Rejected Rejected M × P 0.875 0.0250 0.0333 RetainedRetained SL M 0.534 0.0500 0.0500 Retained Retained P <0.001  0.01670.0167 Rejected Rejected M × P 0.495 0.0250 0.0333 Retained Retained SWVM 0.056 0.0167 0.0167 Retained Retained P 0.325 0.0250 0.0333 RetainedRetained M × P 0.982 0.0500 0.0500 Retained Retained SLV M 0.744 0.05000.0500 Retained Retained P 0.014 0.0167 0.0167 Rejected Rejected M × P0.653 0.0250 0.0333 Retained Retained

Results from the sequential Bonferroni (seqB) and Benjamini-Hochberg(BH) procedures for the data in FIGS. 4 and 5 are presented below inTable C (for positive feedback gains) and Table D (for negative feedbackgains).

In Tables C and D, the following notations are used: M: magnitude, P:Phase, M×P: the interaction between Magnitude and Phase. a_(adj)seqB=theadjusted alpha level with the sequential Bonferroni procedure;a_(adj)BH=the adjusted alpha level with the Benjamini-Hochbergprocedure; H₀seqB=evaluation of the null hypotheses with the sequentialBonferroni procedure; H₀BH=evaluation of the null hypotheses with theBenjamini-Hochberg procedure.

TABLE C Positive feedback gains (G⁺) Effect p value α_(adj)seqBα_(adj)BH H₀ seqB H₀ BH C7 M 0.647 0.0250 0.0333 Retained Retained Step-P 0.854 0.0500 0.0500 Retained Retained step range M × P 0.054 0.01670.0167 Retained Retained C7 M 0.056 0.0167 0.0167 Retained Retained ML P0.294 0.0250 0.0333 Retained Retained RMS M × P 0.369 0.0500 0.0500Retained Retained

TABLE D Negative feedback gains (G⁻) Effect p value α_(adj)seqBα_(adj)BH H₀ seqB H₀ BH C7 M 0.054 0.0167 0.0167 Retained Retained Step-P 0.125 0.0250 0.0333 Retained Retained step range M × P 0.867 0.05000.0500 Retained Retained C7 M 0.113 0.0167 0.0167 Retained Retained ML P0.552 0.0500 0.0500 Retained Retained RMS M × P 0.476 0.0250 0.0333Retained Retained

The disclosure of each of the following references is incorporatedherein by reference in its entirety.

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It will be understood that various details of the presently disclosedsubject matter may be changed without departing from the scope of thepresently disclosed subject matter. Furthermore, the foregoingdescription is for the purpose of illustration only, and not for thepurpose of limitation.

What is claimed is:
 1. A method comprising: displaying a dynamic virtualenvironment defined by an optical flow; obtaining position data of ananatomical portion of a subject that is virtually traversing the dynamicvirtual environment; using the position data to determine a mediolateraldisplacement measurement of the subject; utilizing the mediolateraldisplacement measurement to define feedback control loop data;establishing an augmented visual error that dynamically adjusts thedynamic virtual environment, wherein the augmented visual error is basedon the feedback control loop data and a predefined visual gain factor;and adjusting the optical flow of the dynamic virtual environment byusing the augmented visual error.
 2. The method of claim 1 wherein thedynamic virtual environment includes a virtual hallway.
 3. The method ofclaim 1 wherein a foreground mediolateral position of the dynamicvirtual environment is adjusted by the augmented visual error.
 4. Themethod of claim 1 wherein the mediolateral displacement measurement isbased on a difference of a mediolateral position of the dynamic virtualenvironment and a mediolateral position marker of the subject.
 5. Themethod of claim 4 wherein the anatomical portion of the subjectcorresponds to at least one of a seventh cervical vertebrae of thesubject, a global head position of the subject, and a global trunkposition of the subject.
 6. The method of claim 1 wherein augmentedvisual error defines a difference between a virtual perception of trunkmotion of the subject and an actual trunk motion of the subject.
 7. Themethod of claim 1 further comprising moving a foreground of the dynamicvirtual environment based on the feedback control loop data and theaugmented visual error by a value of G.
 8. A system comprising: adisplay device configured to display a dynamic virtual environmentdefined by an optical flow; at least one position sensor deviceconfigured to obtain position data of an anatomical portion of a subjectthat is virtually traversing the dynamic virtual environment; and asystem controller device configured to use the position data todetermine a mediolateral displacement measurement of the subject,utilize the mediolateral displacement measurement to define feedbackcontrol loop data, establish an augmented visual error that dynamicallyadjusts the dynamic virtual environment, wherein the augmented visualerror is based on the feedback control loop data and a predefined visualgain factor, and adjust the optical flow of the dynamic virtualenvironment by using the augmented visual error.
 9. The system of claim8 wherein the dynamic virtual environment includes a virtual hallway.10. The system of claim 8 wherein a foreground mediolateral position ofthe dynamic virtual environment is adjusted by the augmented visualerror.
 11. The system of claim 8 wherein the mediolateral displacementmeasurement is based on a difference of a mediolateral position of thedynamic virtual environment and a mediolateral position marker of thesubject.
 12. The system of claim 11 wherein the anatomical portion ofthe subject corresponds to at least one of a seventh cervical vertebraeof the subject, a global head position of the subject, and a globaltrunk position of the subject.
 13. The system of claim 8 wherein theaugmented visual error defines a difference between a virtual perceptionof trunk motion of the subject and an actual trunk motion of thesubject.
 14. The system of claim 8 wherein the system controller deviceis further configured to move a foreground of the dynamic virtualenvironment based on the feedback control loop data and the augmentedvisual error by a value of G.
 15. A non-transitory computer readablemedium having stored thereon executable instructions that when executedby a processor of a computer control the computer to perform stepscomprising: displaying a dynamic virtual environment defined by anoptical flow; obtaining position data of an anatomical portion of asubject that is virtually traversing the dynamic virtual environment;using the position data to determine a mediolateral displacementmeasurement of the subject; utilizing the mediolateral displacementmeasurement to define feedback control loop data; establishing anaugmented visual error that dynamically adjusts the dynamic virtualenvironment, wherein the augmented visual error is based on the feedbackcontrol loop data and a predefined visual gain factor; and adjusting theoptical flow of the dynamic virtual environment by using the augmentedvisual error.
 16. The non-transitory computer readable medium of claim15 wherein the dynamic virtual environment includes a virtual hallway.17. The non-transitory computer readable medium of claim 15 wherein aforeground mediolateral position of the dynamic virtual environment isadjusted by the augmented visual error.
 18. The non-transitory computerreadable medium of claim 15 wherein the mediolateral displacementmeasurement is based on a difference of a mediolateral position of thedynamic virtual environment and a mediolateral position marker of thesubject.
 19. The non-transitory computer readable medium of claim 18wherein the anatomical portion of the subject corresponds to at leastone of a seventh cervical vertebrae of the subject, a global headposition of the subject, and a global trunk position of the subject. 20.The non-transitory computer readable medium of claim 15 wherein theaugmented visual error defines a difference between a virtual perceptionof trunk motion of the subject and an actual trunk motion of thesubject.